import pandas as pd
import seaborn as sns
import plotly.express as px
import numpy as np
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
fig, ax = plt.subplots()
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'], 'r')
ax.set_xticks(stocks.loc[::10,'date'])
#make the figure longer
fig.set_figwidth(10)
#display date vertically
plt.xticks(rotation=90)
# set title
ax.set_title('Evolution of Netflix stocks')
# horizontal axis
ax.set_xlabel('Date')
# vertical axis
ax.set_ylabel('Stock value (% of initial value)')
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
# YOUR CODE HERE
fig, ax = plt.subplots()
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'GOOG'], 'c', label='GOOG')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AAPL'], 'grey', label='AAPL')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AMZN'], 'y', label='AMZN')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'FB'], 'b', label='FB')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'], 'r', label='NFLX')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'MSFT'], 'g', label='MSFT')
ax.set_xticks(stocks.loc[::10,'date'])
#make the figure longer
fig.set_figwidth(10)
#display date vertically
plt.xticks(rotation=90)
# set title
ax.set_title('Evolution of GAFAM + Netflix stocks')
# horizontal axis
ax.set_xlabel('Date')
# vertical axis
ax.set_ylabel('Stock value (% of initial value)')
#legend
plt.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
# YOUR CODE HERE
# QUESTION : Which day of the week is the most profitable tip-wise ?
# ANSWER : Show the tip repartition by day
fig, ax = plt.subplots()
sns.boxplot(x='day', y='tip', data=tips, showmeans=True)
ax.set_ylabel('Tip (in euro)')
plt.show()
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE
fig = px.line(stocks, x="date", y=['GOOG', 'AAPL', 'AMZN', 'FB', 'MSFT', 'NFLX'], markers=True)
fig.show()
# YOUR CODE HERE
fig = px.box(tips, x='day', y='tip')
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.reset_index()
fig = px.bar(df_2007_new, x="pop", y='continent', orientation='h', color = 'continent', text = 'pop')
fig.update_yaxes(categoryorder="min ascending")
fig.show()